Method for predicting EUV dose, method for designing photoacid generator, and method for manufacturing semiconductor device

Abstract

Embodiments of the present disclosure provide a method of predicting an extreme ultraviolet dose, a method of designing a photoacid generator, and a method of manufacturing a semiconductor device. In the method for predicting the extreme ultraviolet dose, the whole photochemical reaction mechanism of photoacid generator molecules from photoacid generator cations to release protons under extreme ultraviolet exposure can be analyzed. The lowest unoccupied molecular orbital energy level can be obtained by performing a simulation for structural optimization of photoacid generator cations. Additional parameters different from the lowest unoccupied molecular orbital level may be obtained by performing a simulation for structural optimization of at least one intermediate molecular structure formed by the entire photochemical reaction mechanism. A two-parameter linear regression model for predicting the extreme ultraviolet dose may be obtained based on the lowest unoccupied molecular orbital level and additional parameters.

Description

Technical Field

[0001] Example embodiments relate generally to semiconductor integrated circuits, and more particularly, to a method of predicting an extreme ultraviolet (EUV) dose, a method of designing a photoacid generator (PAG) by using the method of predicting an EUV dose, and a method of manufacturing a semiconductor device by using the method of designing a PAG. Background Art

[0002] When manufacturing highly integrated semiconductor devices, pattern miniaturization is necessary. Recently, as the design of semiconductor devices is rapidly reduced in size, various lithography techniques for realizing fine patterns have been developed. For example, extreme ultraviolet (EUV) lithography using an exposure process through EUV with a wavelength of about 13.5 nanometers (nm) is a next-generation technology to replace the lithography process using KrF excimer lasers (248nm) and ArF excimer lasers (193nm), and various studies are being conducted on EUV lithography.

[0003] The EUV lithography process has a different mechanism of action from the lithography process using KrF excimer lasers and ArF excimer lasers. In the EUV lithography process, the entire process is performed under vacuum. In the EUV lithography equipment, the power required to irradiate the laser from the light source may be insufficient, so there may be a limitation in sufficiently increasing the dose during exposure to generate the required amount of acid from the photoacid generator in the photoresist material. Therefore, a photoacid generator with high acid generation efficiency and dose improvement effect is required. Summary of the invention

[0004] At least one example embodiment of the present disclosure provides a method for efficiently predicting extreme ultraviolet (EUV) dose by photoacid generator (PAG).

[0005] At least one example embodiment of the present disclosure provides a method of efficiently designing a photoacid generator (PAG) with improved lithography characteristics by using a method of predicting an EUV dose.

[0006] At least one example embodiment of the present disclosure provides a method of efficiently manufacturing a semiconductor device by using a method of designing a PAG.

[0007] According to an example embodiment, a method for predicting an extreme ultraviolet (EUV) dose may be performed by running a program code by at least one processor, and the program code may be stored in a non-transitory computer-readable medium. The method may include: analyzing the entire photochemical reaction mechanism of a photoacid generator (PAG) molecule from a PAG cation to a released proton under EUV exposure; obtaining a lowest unoccupied molecular orbital (LUMO) energy level by performing a simulation for structural optimization of the PAG cation; obtaining an additional parameter different from the LUMO energy level by performing a simulation for structural optimization of at least one intermediate molecular structure formed by the entire photochemical reaction mechanism; and obtaining a dual-parameter linear regression model for predicting EUV dose based on the LUMO energy level and the additional parameter.

[0008] According to an example embodiment, a method for designing a photoacid generator (PAG) may be performed by running a program code by at least one processor, and the program code may be stored in a non-transitory computer-readable medium. The method may include: using a first PAG molecule to predict a first extreme ultraviolet (EUV) dose in an EUV lithography process based on an EUV lithography process; using a second PAG molecule to predict a second EUV dose in an EUV lithography process based on an EUV lithography process, the second PAG molecule being different from the first PAG molecule; and determining the suitability of the second PAG molecule based on the first EUV dose and the second EUV dose. Predicting the first EUV dose may include: analyzing a first entire photochemical reaction mechanism of a first PAG molecule from a first PAG cation to release a first proton under EUV exposure; obtaining a first lowest unoccupied molecular orbital (LUMO) energy level by performing a simulation for structural optimization of the first PAG cation; obtaining a first additional parameter different from the first LUMO energy level by performing a simulation for structural optimization of at least one first intermediate molecular structure formed by the first entire photochemical reaction mechanism; and obtaining a first two-parameter linear regression model for predicting a first EUV dose based on the first LUMO energy level and the first additional parameter.

[0009] According to an example embodiment, a method for predicting an extreme ultraviolet (EUV) dose may be performed by running a program code by at least one processor, and the program code may be stored in a non-transitory computer-readable medium. The method may include: analyzing the entire photochemical reaction mechanism of a photoacid generator (PAG) molecule from a PAG cation to a proton release under EUV exposure; obtaining a lowest unoccupied molecular orbital (LUMO) energy level by performing a simulation for structural optimization from a PAG cation to a first molecular structure; obtaining a total energy change from a third molecular structure to a fifth molecular structure by performing a simulation for structural optimization from a third molecular structure to a fifth molecular structure; and obtaining a dual-parameter linear regression model for predicting the EUV dose based on the LUMO energy level and the total energy change. In the entire photochemical reaction mechanism: a first molecular structure can be formed by electron capture; a second molecular structure and a second-first molecular structure can be formed by internal excitation; a second-second molecular structure can be formed by the second molecular structure or the second-first molecular structure; a third molecular structure can be formed by the first molecular structure; a fourth molecular structure can be formed by the second molecular structure, the second-first molecular structure, or the third molecular structure; a fifth molecular structure can be formed by the fourth molecular structure or the second-second molecular structure, and the fifth molecular structure can correspond to the molecular structure after the proton is released from the PAG cation. Obtaining additional parameters may include: obtaining an oxidation energy change by performing a simulation for structural optimization of the third molecular structure; obtaining a deprotonation energy change by performing a simulation for structural optimization of the fourth molecular structure; and calculating a total energy change based on the oxidation energy change and the deprotonation energy change. The PAG cation may be a triphenylsulfonium (TPS) cation, or the PAG cation may have a structure in which at least a portion of each phenyl ring included in the TPS cation is modified.

[0010] According to example embodiments, a method of manufacturing a semiconductor device may include: designing a photoacid generator (PAG) used in an extreme ultraviolet (EUV) lithography process by running a program code by at least one processor, the program code being stored in a non-transitory computer-readable medium; and fabricating a semiconductor device based on a result of designing the PAG. Designing the PAG may include: predicting a first EUV dose in an EUV lithography process when a first PAG molecule is used; predicting a second EUV dose in an EUV lithography process when a second PAG molecule different from the first PAG molecule is used; and determining suitability of the second PAG molecule based on the first EUV dose and the second EUV dose. Predicting a first EUV dose may include: analyzing a first entire photochemical reaction mechanism of a first PAG molecule from a first PAG cation to releasing a first proton under EUV exposure; obtaining a first lowest unoccupied molecular orbital (LUMO) energy level by performing simulation on structural optimization of the first PAG cation; obtaining a first additional parameter different from the first LUMO energy level by performing simulation on structural optimization of at least one first intermediate molecular structure formed by the first entire photochemical reaction mechanism; and obtaining a first two-parameter linear regression model for predicting the first EUV dose based on the first LUMO energy level and the first additional parameter.

[0011] In the method for predicting EUV dose, the method for designing PAG, and the method for manufacturing semiconductor devices according to example embodiments, the photochemical reaction and acid production process of PAG cations in both the "electron capture" mechanism and the "internal excitation" mechanism can be systematically studied using atomic scale molecular modeling. For example, the entire photoreaction mechanism of PAG cations under EUV exposure can be studied, and key parameters for acid production and EUV exposure dose (e.g., additional parameters that affect EUV dose in addition to the LUMO energy level) can be found. In addition, a dual-parameter-based prediction model for PAG performance for EUV exposure dose can be developed based on experimental data. Finally, design rules for synthesizing new EUV PAG materials with excellent properties can be suggested.

[0012] When a dual-parameter linear regression model for EUV dose is obtained using the LUMO energy level and additional parameters according to an example embodiment, the EUV dose can be efficiently predicted, and the prediction consistency can be improved or enhanced based on the dual-parameter linear regression model. In addition, a new PAG molecule can be efficiently designed based on the method for predicting EUV dose according to an example embodiment, and the new PAG molecule has excellent performance (e.g., high acid production rate) compared to the existing PAG molecule and can form a pattern even at a low EUV dose in the EUV lithography process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Illustrative, non-limiting example embodiments will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings.

[0014] Figure 1 is a flow chart illustrating a method of predicting extreme ultraviolet (EUV) dose according to example embodiments.

[0015] Figure 2 and Figure 3 is a block diagram illustrating a prediction and design system according to an example embodiment.

[0016] Figure 4 and Figure 5 is a diagram for describing a method of predicting an EUV dose according to example embodiments.

[0017] Figure 6 , Figure 7 , Figure 8 and Fig. 9 It's a picture. Figure 1 Flowchart of an example of obtaining additional parameters.

[0018] Fig.10 is a flow chart illustrating a method of designing a photoacid generator (PAG) according to example embodiments.

[0019] Fig.11 It's a picture. Fig.10 Flowchart of an example of determining the suitability of a second PAG molecule.

[0020] Fig. 12A , Fig. 12B , Fig.13A and Fig. 13B is a diagram for describing a method of designing a PAG according to example embodiments.

[0021] Fig.14 is a flowchart illustrating a method of manufacturing a semiconductor device according to example embodiments.

[0022] Fig.15A and Fig. 15B are diagrams for describing a method of manufacturing a semiconductor device according to example embodiments. DETAILED DESCRIPTION

[0023] Various example embodiments will be described more fully with reference to the accompanying drawings, in which embodiments are shown. However, the present disclosure may be embodied in a variety of different forms and should not be construed as being limited to the embodiments set forth herein. Throughout this application, the same reference numerals refer to the same elements.

[0024] Figure 1 is a flow chart illustrating a method of predicting extreme ultraviolet (EUV) dose according to example embodiments.

[0025] refer to Figure 1 , the method for predicting an extreme ultraviolet (EUV) dose according to an example embodiment may be performed in a design process for a photoacid generator (PAG) that is included in a specific material used in a manufacturing process of a semiconductor device (or semiconductor integrated circuit). For example, the material including the PAG may include a photoresist used in an EUV lithography process. For example, the method for predicting an EUV dose according to an example embodiment may be performed on a system and / or tool for performing atomic scale molecular (or material) simulations. For example, a system and / or tool for performing atomic scale molecular simulations may include a program (or program code) having a plurality of instructions executed by at least one processor. Reference will be made to Figure 2 and Figure 3 Describe the system and / or tool.

[0026] In the method of predicting EUV dose (or EUV exposure dose) according to example embodiments, the entire (or overall) photochemical reaction mechanism of PAG molecules from PAG cations (or PAG cation molecules) to release of protons under EUV exposure is analyzed (operation S100). Figure 4 and Figure 5 Describe the overall photochemical reaction mechanism.

[0027] In some example embodiments, the PAG cation may be or may include a triphenylsulfonium (TPS) cation (or a TPS cation molecule) that has been widely used. In some example embodiments, the PAG cation may have a structure in which at least a portion of the TPS cation is modified, for example, a structure in which each phenyl ring or at least a portion of the phenyl ring included in the TPS cation is modified. Fig. 12A and Fig. 12B Examples of PAG cations are described.

[0028] The lowest unoccupied molecular orbital (LUMO) energy level is obtained by performing simulation for structural optimization of the PAG cation (operation S200 ). The LUMO energy level may be one of the key parameters (or factors or variables) known to affect the EUV dose.

[0029] The additional parameter other than the LUMO energy level is obtained by performing simulation for structural optimization of at least one intermediate molecular structure formed by the entire photochemical reaction mechanism (operation S300). The additional parameter may be a parameter that affects the EUV dose in addition to the LUMO energy level. Figure 4 Examples of additional parameters associated or relevant to the overall photochemical reaction mechanism are described and will be referenced Figures 6 to 9 An example of operation S300 is described.

[0030] A two-parameter linear regression model for predicting EUV dose is obtained based on the LUMO level and the additional parameters (operation S400). For example, the two-parameter linear regression model can be obtained by Equation 1.

[0031] [Formula 1]

[0032] Dose=α×LUMO+β×P after +γ

[0033] In Formula 1, “Dose” represents EUV dose, “LUMO” represents LUMO energy level, and P after represents an additional parameter, and α, β, and γ represent coefficients, respectively. For example, α may correspond to the contribution of the LUMO energy level, and β may correspond to the contribution of the additional parameter.

[0034] In the manufacturing process of semiconductor devices, the photolithography process (or simply the photolithography process) is a patterning process for thin films by exposing a photosensitive material (e.g., photoresist) to light and then selectively dissolving it to make the desired shape on a nanometer length scale. The minimum feature size, which is defined as the minimum size of each element on a chip, such as the distance between the source and drain of a transistor, can be reduced by reducing the wavelength of the light used in the photolithography process. Conventional photolithography processes using deep ultraviolet (DUV) light with a wavelength of about 248nm (from a KrF excimer laser) and a wavelength of about 193nm (from an ArF excimer laser) allow the minimum feature size to be reduced to about 50nm. In order to further scale down the minimum feature size, EUV lithography technology using EUV with a wavelength of about 13.5nm has been developed, and recently, the minimum feature size can be scaled down to about 10nm in a single exposure patterning.

[0035] One of the main challenges in the EUV lithography process is the "randomness problem" in which photons are sparsely distributed in the exposure area. Therefore, a high EUV exposure dose (e.g., low sensitivity) may be required to avoid degradation of line width roughness (LWR), line edge roughness (LER), resolution, etc. However, compared with conventional DUV systems, EUV exposure systems may incur higher costs due to high power density and high maintenance costs. As a result, EUV exposure systems may result in low throughput and low productivity. Therefore, for mass production (or mass manufacturing), it is necessary to develop high-performance photoresist materials that can form patterns even at low EUV exposure doses (e.g., high sensitivity).

[0036] Similar to KrF / ArF technology, EUV photoresist is usually composed of a chemically amplified resist (CAR) complex, which is composed of a polymer, a photoacid generator (PAG) and a quencher. However, the photon energy of EUV light (e.g., about 92eV) can be much higher than the photon energy of KrF light (e.g., about 5eV) and the photon energy of ArF light (e.g., about 6.4eV). Therefore, the photochemical process of EUV photoresist can be different from those photochemical processes in previous KrF / ArF photoresists. Currently, in the widely accepted photochemical process of EUVCAR, the polymer resin as the main part of the resist can absorb high-energy EUV photons and then generate secondary electrons. PAG molecules can absorb these secondary electrons and generate acid. Then, the protecting group of the polymer can be replaced by a hydroxyl group, and the deprotection reaction is catalyzed by an acid. As a result, the polymer can change its solubility in a given developer. When the acid production rate is increased, a lower EUV dose can be used without degrading the resolution. Therefore, the sensitivity of EUV photoresist can be related to the acid generation rate of CAR.

[0037] For the acid generation process of CAR in EUV lithography process, there are two proposed mechanisms derived from PAG cation reaction. First, high-energy (e.g., about 10eV to 82eV) secondary electrons can lose their energy to excite electrons from the highest occupied molecular orbital (HOMO) of the PAG molecule to the antibonding lowest unoccupied molecular orbital (LUMO). Then, this excited PAG molecule can dissociate to produce acid. Otherwise, low-energy (e.g., below about 10eV) secondary electrons can be captured by the antibonding LUMO of the PAG molecule, and it can be considered to cause PAG decomposition and acid generation. In addition, recent EUV lithography experiments on CARs with non-phenolic or anionic bound polymers have demonstrated that the PAG cation reaction is the most important step for acid generation. However, the detailed physical and chemical mechanisms of acid generation from PAG cation molecules are still poorly understood.

[0038] According to example embodiments, the photochemical reaction and acid production process of PAG cations in both the "electron capture" mechanism and the "internal excitation" mechanism can be systematically studied using atomic scale molecular modeling to develop an EUV sensitivity prediction model. The method for predicting EUV dose and the method for designing PAG according to example embodiments can be achieved by targeting triphenylsulfonium (Ph3S+ / TPS) cations (which are conventional PAG cation molecules of photoresist components) and their derivative cations with symmetrical and asymmetrical functionalized substituents. For example, the entire photoreaction mechanism of PAG cations under EUV exposure can be studied, and key parameters for acid production and EUV exposure dose (e.g., additional parameters that affect EUV dose in addition to LUMO energy level) can be found. In addition, a dual-parameter-based prediction model for EUV exposure dose to PAG performance can be developed based on experimental data. Finally, design rules for synthesizing new EUV PAG materials with excellent properties can be suggested.

[0039] Figure 2 and Figure 3 is a block diagram illustrating a prediction and design system according to an example embodiment.

[0040] refer to Figure 2 The prediction and design system 1000 includes a processor 1100 , a storage device 1200 , and a prediction and design module 1300 .

[0041] As used herein, the term "module" may indicate, but is not limited to, software and / or hardware components that perform certain tasks, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A module may be configured to reside in a tangible addressable storage medium and to run on one or more processors. For example, a "module" may include components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, routines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. A "module" may be divided into multiple "modules" that perform detailed functions.

[0042] When the prediction and design module 1300 performs calculations or operations, the processor 1100 may be used. For example, the processor 1100 may include a microprocessor, an application processor (AP), a central processing unit (CPU), a digital signal processor (DSP), a graphics processing unit (GPU), a neural processing unit (NPU), etc. Figure 2 The prediction and design system 1000 is illustrated to include one processor 1100, but example embodiments are not limited thereto. For example, the prediction and design system 1000 may include a plurality of processors. In addition, the processor 1100 may include a cache memory to increase computing capacity.

[0043] The storage device 1200 may store data for the operation of the processor 1100 and the prediction and design module 1300. In some example embodiments, the storage device (or storage medium) 1200 may include any non-transitory computer-readable storage medium for providing commands and / or data to a computer. For example, the non-transitory computer-readable storage medium may include volatile memories such as static random access memory (SRAM), dynamic random access memory (DRAM), etc., and non-volatile memories such as flash memory, magnetic random access memory (MRAM), phase change random access memory (PRAM), resistive random access memory (RRAM), etc. The non-transitory computer-readable storage medium may be inserted into the computer, may be integrated into the computer, or may be coupled to the computer via a communication medium such as a network and / or a wireless link.

[0044] The prediction and design module 1300 can perform reference Figure 1 The method for predicting EUV dose according to example embodiments described herein may be performed with reference to Fig.10 The method of designing a PAG according to an example embodiment is described. The prediction and design module 1300 may include an analysis module 1310 , a simulation module 1320 , and a determination module 1330 .

[0045] The analysis module 1310 may perform an analysis operation to perform a method of predicting EUV dose and / or a method of designing a PAG according to example embodiments. For example, the analysis module 1310 may analyze the entire photochemical reaction mechanism of the PAG and may analyze various other data.

[0046] The simulation module 1320 may perform simulation operations to perform a method for predicting EUV dose and / or a method for designing a PAG according to example embodiments. For example, the simulation module 1320 may perform simulations for structural optimization of a molecular structure, and may perform various operations, processing, data generation and storage, etc. related thereto.

[0047] The determination module 1330 may perform a determination (or judgment) operation and a result generation operation to perform a method for predicting EUV dose and / or a method for designing a PAG according to example embodiments. For example, the determination module 1330 may generate a linear regression model based on the results of the analysis and simulation operations, and may determine the suitability of a newly designed (or proposed) PAG structure.

[0048] For example, the analysis module 1310 may execute Figure 1 In operation S100, the simulation module 1320 may perform Figure 1 Operations S200 and S300 in the embodiment, and the determination module 1330 may perform Figure 1For example, the analysis module 1310, the simulation module 1320 and the determination module 1330 may perform Fig.10 Operations S1100 and S1200 in the embodiment, and the determination module 1330 may perform Fig.10 Operation S1300 in .

[0049] In some example embodiments, the analysis module 1310, the simulation module 1320, and the determination module 1330 may be implemented as instructions or program codes that may be executed by the processor 1100. For example, the instructions or program codes of the analysis module 1310, the simulation module 1320, and the determination module 1330 may be stored in a computer-readable medium. For example, the processor 1100 may load the instructions or program codes into a working memory (e.g., DRAM, etc.).

[0050] In other example embodiments, the processor 1100 may be manufactured to efficiently run the instructions or program codes included in the analysis module 1310, the simulation module 1320, and the determination module 1330. For example, the processor 1100 may efficiently run instructions or program codes from various artificial intelligence (AI) modules and / or machine learning modules. For example, the processor 1100 may receive information corresponding to the analysis module 1310, the simulation module 1320, and the determination module 1330 to operate the analysis module 1310, the simulation module 1320, and the determination module 1330.

[0051] In some example embodiments, the analysis module 1310, the simulation module 1320, and the determination module 1330 may be implemented as a single integrated module. In other example embodiments, the analysis module 1310, the simulation module 1320, and the determination module 1330 may be implemented as separate and distinct modules.

[0052] refer to Figure 3 , the prediction and design system 2000 includes a processor 2100 , an input / output (I / O) device 2200 , a network interface 2300 , a random access memory (RAM) 2400 , a read-only memory (ROM) 2500 , and a storage device 2600 . Figure 3 Pictured Figure 2 An example in which all of the analysis module 1310, the simulation module 1320, and the determination module 1330 are implemented using software.

[0053] The prediction and design system 2000 may be a computing system, which may be a fixed computing system such as a desktop computer, a workstation, or a server, or a portable computing system such as a laptop computer.

[0054] The processor 2100 can be used with Figure 2The processor 2100 may be the same or substantially the same as the processor 1100 in the embodiment. For example, the processor 2100 may include a core or processor core for executing any instruction set (e.g., Intel Architecture-32 (IA-32), 64-bit extended IA-32, x86-64, PowerPC, Sparc, MIPS, ARM, IA-64, etc.). For example, the processor 2100 may access a memory (e.g., RAM 2400 or ROM 2500) through a bus and may execute instructions stored in the RAM 2400 or ROM 2500. Figure 3 As shown, RAM 2400 may store Figure 2 The program PR or at least some elements of the program PR corresponding to the analysis module 1310, the simulation module 1320, and the determination module 1330 in the processor 2100 may enable the processor 2100 to perform operations for predicting EUV dose and / or designing PAG according to example embodiments (e.g., Figure 1 Operations S100, S200, S300 and S400 and / or Fig.10 Operations S1100, S1200 and S1300 in FIG.

[0055] In other words, the program PR may include a plurality of instructions and / or procedures that may be executed by the processor 2100, and the plurality of instructions and / or procedures included in the program PR may allow the processor 2100 to perform operations for predicting EUV dose and / or designing PAG according to example embodiments. Each of the procedures may represent a series of instructions for performing a certain task. A procedure may be referred to as a function, a routine, a subroutine, or a subprogram. Each of the procedures may process data provided from the outside and / or data generated by another procedure.

[0056] In some example embodiments, the RAM 2400 may include any volatile memory such as SRAM, DRAM, or the like.

[0057] The storage device 2600 can be used with Figure 2 2400. For example, the storage device 2600 may store the program PR. The program PR or at least some elements of the program PR may be loaded from the storage device 2600 to the RAM 2400 before being executed by the processor 2100. The storage device 2600 may store a file written in a programming language, and the program PR or at least some elements of the program PR generated by a compiler or the like may be loaded to the RAM 2400.

[0058] The storage device 2600 may store data to be processed by the processor 2100 or data obtained by being processed by the processor 2100. The processor 2100 may process the data stored in the storage device 2600 based on the program PR to generate new data, and may store the generated data in the storage device 2600.

[0059] The I / O device 2200 may include input devices such as a keyboard, a pointing device, etc., and may include output devices such as a display device, a printer, etc. For example, a user may trigger the processor 2100 to run the program PR through the I / O device 2200, and may provide or check various inputs, outputs, and / or data, etc.

[0060] The network interface 2300 may provide access to a network external to the prediction and design system 2000. For example, the network may include multiple computing systems and communication links, and these communication links may include wired links, optical links, wireless links, or any other type of links. Various inputs may be provided to the prediction and design system 2000 through the network interface 2300, and various outputs may be provided to another computing system through the network interface 2300.

[0061] In some example embodiments, the computer program code, analysis module 1310, simulation module 1320, and / or determination module 1330 may be stored in a temporary or non-transitory computer-readable medium. In some example embodiments, values ​​generated from a simulation performed by a processor or values ​​obtained from an arithmetic process performed by a processor may be stored in a temporary or non-transitory computer-readable medium. In some example embodiments, intermediate values ​​generated during the simulation may be stored in a temporary or non-transitory computer-readable medium. In some example embodiments, various data associated or related to the simulation may be stored in a temporary or non-transitory computer-readable medium. However, example embodiments are not limited thereto.

[0062] Figure 4 and Figure 5 is a diagram for describing a method of predicting an EUV dose according to example embodiments.

[0063] refer to Figure 4 , illustrates the entire photochemical reaction mechanism of PAG molecules from PAG cation to release of protons under EUV exposure. For example, the PAG cation can be a TPS cation, and Figure 4 The diagram shows the energy distribution of the photochemical reaction of TPS cations under EUV exposure. Figure 4 In the figure, the relative energy values ​​of TPS cations are shown in eV units.

[0064] As described above, the mechanism of generating acid from PAG can include "electron capture" and "internal excitation". As the band gap becomes narrower, internal excitation may be easier (for example, acid can be more easily generated by internal excitation) because electrons can be easily excited to the LUMO energy level. In addition, as the LUMO energy level decreases more, secondary electrons can be easily captured in the LUMO energy level. Therefore, the efficiency of electron capture and internal excitation processes can strongly depend on the band gap and LUMO energy level. In order to understand the overall deprotonation process of PAG cations, the detailed EUV photochemical reaction of TPS cations as conventional PAG cations was studied. For example, TPS molecules can be decomposed into diphenyl sulfide (or diphenyl sulfide) (DPS) and phenyl radicals. Then, the TPS that is excited or captures electrons can release acid by deprotonation during rearrangement.

[0065] In some example embodiments, in the entire photochemical reaction mechanism of releasing protons from the PAG cation, a first molecular structure can be formed by electron capture; a second molecular structure and a second-first molecular structure can be formed by internal excitation; a second-second molecular structure can be formed from the second molecular structure or the second-first molecular structure; a third molecular structure can be formed from the first molecular structure; a fourth molecular structure can be formed from the second molecular structure, the second-first molecular structure, or the third molecular structure; and a fifth molecular structure can be formed from the fourth molecular structure or the second-second molecular structure and the fifth molecular structure corresponds to the molecular structure after the proton is released from the PAG cation.

[0066] First, the mechanism associated with electron capture can be described as follows. When the TPS cation captures a secondary electron with relatively low energy (e.g., less than about 10 eV), the TPS cation can be spontaneously dissociated into a stable DPS molecule and a phenyl radical by electron capture, thereby forming a first molecular structure. For example, Figure 4 The structure indicated by mark “1” in FIG. 5 may represent a first molecular structure and may have an energy level of approximately −3.955 eV.

[0067] The phenyl radical decomposed from the first molecular structure can attack the ortho position of the benzene ring in the DPS molecule because this is the most sterically favorable, thus forming a third molecular structure. Based not only on kinetic probability but also on thermal stability, 2(ortho)-(phenylthio)biphenyl can be prepared or created as the major product of the redirected phenyl-substituted DPS isomer. For example, Figure 4 The structure indicated by mark '3' in FIG. 5 may represent a third molecular structure and may have an energy level of approximately -4.305 eV.

[0068] After or during the generation of 2(o)-(phenylthio)biphenyl, 2(o)-(phenylthio)biphenyl can reduce other PAG cations and can return to a cation. In other words, the third molecular structure can be oxidized to form a fourth molecular structure. For example, Figure 4 The structure indicated by the mark '4' in FIG. 4 may represent a fourth molecular structure and may have an energy level of approximately -0.637 eV.

[0069] Thereafter, the nearby PAG anion or polymer can react with the ortho-position hydrogen, and finally, a proton can be released from the PAG cation, so that a fifth molecular structure can be formed. For example, Figure 4 The structure indicated by mark '5' in FIG. 5 may represent a fourth molecular structure and may have an energy level of approximately -0.602 eV.

[0070] In addition, the mechanism related to the internal excitation can be described as follows. The coupling between the secondary electrons with relatively high energy can cause the electronic excitation from HOMO to LUMO, and the internal excitation can be induced in the TPS cation. Through this internal excitation, the TPS cation can be dissociated into a DPS radical cation with a phenyl radical, and thus a second molecular structure can be formed. Otherwise, through this internal excitation, the TPS cation can be dissociated into a DPS molecule with a phenyl cation, and thus a second-first molecular structure can be formed. For example, Figure 4 The structure indicated by the mark "2" in FIG. 1 may represent a second molecular structure and may have an energy level of approximately 1.946 eV. For example, Figure 4 The structure indicated by the mark “2-1” in FIG. 2 may represent a second molecular structure and may have an energy level of approximately 2.216 eV.

[0071] The proton may be released from the second molecular structure in which the TPS cation is dissociated into a DPS radical cation with a phenyl radical, or the proton may be released from the second-first molecular structure in which the TPS cation is dissociated into a DPS molecule with a phenyl cation, thereby forming a second-second molecular structure. For example, Figure 4 The structure indicated by the mark “2-2” in FIG. 1 may represent a second-second molecular structure.

[0072] Thereafter, it may proceed from the second molecular structure or the second-first molecular structure to the fourth molecular structure and the fifth molecular structure, or it may proceed from the second-second molecular structure to the fifth molecular structure, and finally, protons may be released from the PAG cation, and thus the fifth molecular structure may be formed.

[0073] Can be based on Figure 4The entire photochemical reaction mechanism illustrated is simulated and thus additional parameters affecting EUV dose in addition to the LUMO energy level can be obtained.

[0074] In some example embodiments, the additional parameters may include one of: a first deprotonation efficiency from the second molecular structure to the second-second molecular structure, a second deprotonation efficiency from the fourth molecular structure to the fifth molecular structure, an oxidation energy change from the third molecular structure to the fourth molecular structure, and a total energy change from the third molecular structure to the fifth molecular structure. Figure 4 In the above, the first deprotonation efficiency is represented by “pKa(DPS)”, the second deprotonation efficiency is represented by “pKa(rearranged)”, and the amount of oxidation energy change is represented by “ΔG oxidation ", and the total energy change is represented by "ΔG total "express.

[0075] For example, atomic scale molecular simulation can be performed based on density functional theory (DFT) implemented in the Gaussian16 program package from Gauss. The optimized structures of neutrons, cations and radical molecules / fragments in the gas phase can be calculated with B3LYP functionals and cc-pvdz atomic basis sets. The Gibbs free energy correction can be included at the B3LYP / 6-31++g(d) level of the single-point frequency calculation of the gas phase optimized structure. In order to include the solvation effect, the solvent conditions can be approximated by the integral equation form variant polarizable continuous model (IEFPCM) with propylene glycol methyl ether acetate (PGMEA) solvent. The Gibbs free energy and solvation effect calibration parameters can be obtained by formula 2.

[0076] [Formula 2]

[0077] ΔG solv =H-TΔS+(ΔE PGMEA -ΔE gas )

[0078] =ΔG gas +(ΔE PGMEA -ΔE gas )

[0079] In Formula 2, ΔG solv represents the Gibbs free energy including solvation effects, ΔG gas represents the Gibbs free energy in the gas phase, H represents the enthalpy of the gas phase, TΔS represents the entropy change of the system, ΔE PGMEA represents the internal energy of the PAG molecule by considering the surrounding PGMEA solvent, and ΔE gasrepresents the internal energy of the PAG molecule in the gas phase. All Gibbs free energy terms can be referred to as solvation effect correction values. In order to obtain the deprotonation efficiency and oxidation barrier of the intermediate molecular structure, the following formula can be defined. For example, the first deprotonation efficiency pKa (DPS), the second deprotonation efficiency pKa (rearranged) and the oxidation energy change ΔG mentioned as additional parameters oxidation It can be obtained by equation 3, equation 4 and equation 5 respectively, and the total energy change ΔG total It can be obtained by Equation 6 and Equation 7.

[0080] [Formula 3]

[0081]

[0082] [Formula 4]

[0083] pKa(rearranged)=(ΔG struct5 +ΔG proton )-ΔG struct4

[0084] [Formula 5]

[0085] ΔG oxidation =ΔG struct4 -ΔG struct3

[0086] [Formula 6]

[0087] ΔG deprotonation =(ΔG struct5 +ΔG proton )-ΔG struct4

[0088] [Formula 7]

[0089] ΔG total =ΔG oxidation +ΔG deprotonation

[0090] =(ΔG struct5 +ΔG proton )-ΔG struct3

[0091] For the above equation, the theoretical Gibbs free energy value of the proton can be used (eg, approximately 262.4 kcal / mol), and a single point energy calculation can be performed to include the effect of PGMEA (eg, ε=9.34) to obtain ΔG proton Additionally, each term may indicate the Gibbs free energy obtained through structural optimization for each molecular structure.

[0092] Another parameter that can represent the proton release ability can be introduced. For example, it can be represented as the pKa of diphenyl sulfide (e.g., DPS) and the pKa of rearranged TPS. Since the pKa parameter can be significantly adjusted by the functional group of the diphenylthiol ether radical cation, it indicates that the pKa parameter of the PAG cation can be another major factor for predicting EUV dose.

[0093] The above formula 1 can be based on one of the above four additional parameters (for example, the first deprotonation efficiency pKa (DPS), the second deprotonation efficiency pKa (rearranged), the oxidation energy change ΔG oxidation and the total energy change ΔG total In other words, in Formula 1, P after Can be pKa(DPS), pKa(rearranged), ΔG oxidation and ΔG total one of them.

[0094] refer to Figure 5 , example embodiments may include not only examples where the PAG cation is a TPS cation, but also examples where the PAG cation can be extended to a modified structure of the TPS cation as illustrated. For example, the PAG cation may have a structure in which each phenyl ring or at least a portion of the phenyl ring included in the TPS cation is modified.

[0095] In order for the PAG cation to be extended to the modified structure of the TPS cation, it is necessary to know the most dissociable phenyl ring among the three phenyl rings included in the TPS cation. In addition, when the model is extended to various asymmetrically substituted TPS compounds other than the triply symmetric TPS (e.g., the TPS cation), it is necessary to know where the most dissociable phenyl ring will be attached.

[0096] First, the single occupied molecular orbital (SOMO) TPS or 1-substituted TPS or 2-substituted TPS can be compared to determine the dissociated phenyl ring. The hydroxyl group (-OH) can represent an electron donating substituent or electron donating substituent (EDG), and the trifluoromethane (-CF 3 ) can represent an electron-withdrawing substituent (EWG). The antibonding orbital can change its position depending on the electron-donating / electron-withdrawing properties of the substituent. However, for all cases, the orbital node can be located on the bond between the sulfur and the phenyl ring with the most electron-rich phenyl ring. As a result, the phenyl ring with relatively more electron-donating groups attached can dissociate from the sulfur center. Due to conjugation, the substituted phenyl ring can dissociate as a free radical fragment, and DPS can still act as a neutral fragment. Next, it may be important that the substituted phenyl radical will attack the more electron-rich site between the two phenyl rings of DPS.

[0097] As described above, regularity when the asymmetric TPS structure dissociates can be obtained by SOMO analysis. Based on this, it can be confirmed that the example embodiments are not only applicable to existing materials (e.g., TPS cations) that have been used in EUV lithography processes, but also to target materials that will be redesigned for use in EUV lithography processes (e.g., modified structures of TPS cations). In other words, based on a two-parameter linear regression model combining the LUMO energy level and one of the four additional parameters, when various structures in which substituents use the TPS molecule as a basic framework to bond or not bond to each carbon of each phenyl ring of a TPS molecule-based material are used in an EUV lithography process, the EUV dose can be efficiently predicted.

[0098] Figure 6 , Figure 7 , Figure 8 and Fig. 9 It's a picture. Figure 1 Flowchart of an example of obtaining additional parameters.

[0099] refer to Figure 1 and Figure 6 When the additional parameters are obtained (operation S300), when the additional parameters include a first deprotonation efficiency from the second molecular structure to the second-second molecular structure, the first deprotonation efficiency can be obtained by performing a simulation for structural optimization of the second molecular structure (operation S310). For example, the first deprotonation efficiency pKa (DPS) can be obtained based on Formula 3.

[0100] refer to Figure 1 and Figure 7 When the additional parameters are obtained (operation S300), when the additional parameters include the second deprotonation efficiency from the fourth molecular structure to the fifth molecular structure, the second deprotonation efficiency can be obtained by performing simulation on the structural optimization of the fourth molecular structure (operation S320). For example, the second deprotonation efficiency pKa (rearranged) can be obtained based on Formula 4.

[0101] refer to Figure 1 and Figure 8 When the additional parameters are obtained (operation S300), when the additional parameters include the oxidation energy change amount from the third molecular structure to the fourth molecular structure, the oxidation energy change amount can be obtained by performing a simulation for the structural optimization of the third molecular structure (operation S330). For example, the oxidation energy change amount ΔG can be obtained based on Formula 5 oxidation .

[0102] refer to Figure 1 and Fig. 9, when the additional parameters are obtained (operation S300), when the additional parameters include the total energy change from the third molecular structure to the fifth molecular structure, the oxidation energy change can be obtained by performing a simulation for structural optimization of the third molecular structure (operation S330), the deprotonation energy change can be obtained by performing a simulation for structural optimization of the fourth molecular structure (operation S340), and the total energy change can be obtained based on the oxidation energy change and the deprotonation energy change (operation S350). Operation S330 can be compared with the reference Figure 8 The operations described are the same or substantially the same. For example, the deprotonation energy change ΔG can be obtained based on Equation 6 deprotonation , and the total energy change ΔG can be obtained based on Equation 7 total .

[0103] based on Fig. 9 Examples (e.g., based on additional parameters including the total energy change ΔG total The detailed process of performing the two-parameter regression analysis can be described as follows. For example, the Gaussian16 program can be used to perform the simulation. However, the example embodiments are not limited thereto, and various quantum chemical calculation tools capable of performing DFT calculations, such as Q-Chem, ADF, NWChem, etc., can be used.

[0104] In operation S200, for the PAG material whose dosage is measured under the same conditions, energy, enthalpy or Gibbs free energy may be calculated, and the solvation effect may be corrected by structural optimization or vibration energy calculation. For example, in this structure, the charge of the system may be +1, and the spin state may be a singlet. For example, after calculating the Gibbs free energy at the B3LYP / cc-pvdz level, the solvation effect by PGMEA may be reflected at the B3LYP / 6-31++g(d) level. The obtained structure (e.g., Figure 4 The molecular orbital energy level calculation is performed using the first molecular structure in ( ). For example, a calculation method including dispersion energy correction can be used as needed. For example, the LUMO energy level of PAG can be obtained at the cam-B3LYP / aug-cc-pvdz level.

[0105] In operation S300, among the three phenyl rings of the PAG (eg, TPS), the phenyl ring with a substituent having the highest electron donating effect (EDG) may be discarded, and this may be caused by Figure 5 The orbital structure shown in the figure. The separated phenyl group can be bonded to the ortho position of the phenyl ring on one side with a substituent having a high electron donating effect in the remaining DPS. The obtained structure (for example, Figure 4The third molecular structure in the above example is used to perform structural optimization or Gibbs free energy calculation. For example, in this structure, since the secondary electrons are trapped, the charge of the system can be 0, and the spin state can be a doublet. For example, the Gibbs free energy obtained in this structure can be ΔG struct3 .

[0106] Next, the obtained structure in which the separated phenyl group is bonded to the ortho position (for example, Figure 4 The oxidation state of the third molecular structure in Figure 4 The fourth molecular structure in the above example can be used to perform a structure optimization or Gibbs free energy calculation. For example, in this structure, the charge of the system can be +1, and the spin state can be a singlet. For example, the Gibbs free energy obtained in this structure can be ΔG struct4 In some example embodiments, obtaining ΔG may be omitted. struct4 operation.

[0107] Thereafter, the proton attached to the phenyl ring at the ortho position may be removed from an oxidized state (e.g., Figure 4 The fourth molecular structure in the final state released (for example, Figure 4 The fifth molecular structure in ( ) performs structural optimization or Gibbs free energy calculation. For example, in this structure, since the protons are released after oxidation, the charge of the system may be 0, and the spin state may be a singlet. For example, the Gibbs free energy obtained in this structure may be ΔG struct5 .

[0108] Finally, the total energy change ΔG can be obtained based on Equation 7 total .

[0109] In operation S400, the energy change amount ΔG obtained in operation S200 and the total energy change amount ΔG obtained in operation S300 may be calculated based on the LUMO energy level obtained in operation S200. total Regression analysis was performed to obtain a two-parameter linear regression model such as Equation 1.

[0110] Fig.10 is a flowchart illustrating a method of designing a photoacid generator (PAG) according to an example embodiment. Figure 1 Duplicate description.

[0111] refer to Fig.10, the method of designing a photoacid generator (PAG) according to an example embodiment may be performed in a design process for a PAG included in a specific material used in a manufacturing process of a semiconductor device (or semiconductor integrated circuit). For example, the material including the PAG may include a photoresist used in an EUV lithography process. For example, the method of designing a PAG according to an example embodiment may be performed on a system and / or tool for performing atomic scale molecular simulations.

[0112] In a method of designing a PAG according to example embodiments, a first EUV dose in an EUV lithography process is predicted when a first PAG molecule is used (operation S1100), and a second EUV dose in the EUV lithography process is predicted when a second PAG molecule different from the first PAG molecule is used (operation S1200).

[0113] The first PAG molecule can be an existing material (or conventional material) that has been used in an EUV lithography process. The second PAG molecule can be a target material (or new material) that is redesigned for use in an EUV lithography process. In other words, the properties of an existing PAG molecule can be used to design and / or develop a new PAG molecule.

[0114] The suitability of the second PAG molecule is determined based on the first EUV dose and the second EUV dose (operation S1300). Fig.11 Operation S1300 is described.

[0115] Can be based on reference Figures 1 to 9 The method of predicting an EUV dose according to example embodiments is described to perform each of operations S1100 and S1200 .

[0116] For example, when predicting the first EUV dose, the first entire photochemical reaction mechanism of the first PAG molecule from the first PAG cation to the release of the first proton under EUV exposure can be analyzed, the first LUMO energy level can be obtained by performing simulation on the structure optimization of the first PAG cation, the first additional parameter different from the first LUMO energy level can be obtained by performing simulation on the structure optimization of at least one first intermediate molecular structure formed by the first entire photochemical reaction mechanism, and the first two-parameter linear regression model for predicting the first EUV dose can be obtained based on the first LUMO energy level and the first additional parameter.

[0117] When predicting the second EUV dose, the second entire photochemical reaction mechanism of the second PAG molecule from the second PAG cation to the release of the second proton under EUV exposure can be analyzed, and the second LUMO energy level can be obtained by performing simulation on the structure optimization of the second PAG cation, and a second additional parameter different from the second LUMO energy level can be obtained by performing simulation on the structure optimization of at least one second intermediate molecular structure formed by the second entire photochemical reaction mechanism, and a second two-parameter linear regression model for predicting the second EUV dose can be obtained based on the second LUMO energy level and the second additional parameter.

[0118] Fig.11 It's a picture. Fig.10 Flowchart of an example of determining the suitability of a second PAG molecule.

[0119] refer to Fig.10 and Fig.11 , when the suitability of the second PAG molecule is determined (operation S1300 ), it may be determined whether the second EUV dose has an improvement (or is improved) compared to the first EUV dose (operation S1310 ).

[0120] When the second EUV dose has an improvement over the first EUV dose (operation S1310: yes), for example, when the second PAG molecule has a higher acid production rate and maintains or improves the productivity of the semiconductor device even if the second EUV dose becomes lower, it can be determined that the second PAG molecule is suitable for use in an EUV lithography process (operation S1320). In this case, the second PAG molecule can be manufactured, the second PAG molecule can be used in a manufacturing process (e.g., an EUV lithography process), and the newly developed and / or proposed second PAG molecule can be used to manufacture semiconductor devices.

[0121] When the second EUV dose is not improved compared to the first EUV dose (operation S1310: No), for example, when the acid production rate of the second PAG molecule is not improved and if the productivity of the semiconductor device is maintained only when the second EUV dose is higher than or equal to the first EUV dose, it can be determined that the second PAG molecule is not suitable for use in the EUV lithography process (operation S1330). In this case, the structure of the second PAG molecule can be changed (operation S1340), and operations S1200 and S1300 can be re-performed for the second PAG molecule whose structure has been changed. In other words, a new PAG molecule can be developed and / or proposed again.

[0122] Fig. 12A , Fig. 12B , Fig.13A and Fig. 13B is a diagram for describing a method of designing a PAG according to example embodiments.

[0123] refer to Fig. 12A , the TPS cation is illustrated as an example of a first PAG molecule. The TPS cation may have a reference Figure 4 Describe the entire photochemical reaction mechanism.

[0124] refer to Fig. 12B , a three-fold symmetrical TPS cation with a substituent is illustrated as an example of a first PAG molecule. For example, X can be -OH, -OMe, -tBu, -iPr, -Et, -Me, -H, -F, -I, -COMe, -CF 3 , -CN and -NO 2 one of them.

[0125] As Fig. 12A and Fig. 12B A modified structure of the illustrated molecular structure may be developed and / or proposed as a structure in which at least a portion of each phenyl ring is modified (e.g., one of various structures in which a substituent is bonded or not bonded to each carbon of each phenyl ring). Fig.10 The second PAG molecule in the embodiment of the present invention is a second PAG molecule, and the performance of the second PAG molecule can be verified by simulation.

[0126] refer to Fig.13A , which illustrates the above four additional parameters (eg, the first deprotonation efficiency pKa (DPS), the second deprotonation efficiency pKa (rearranged), the oxidation energy change ΔG oxidation and the total energy change ΔG total )'s regression analysis results.

[0127] Compared with the results of predicting EUV dose using only LUMO energy level, it can be seen that the results of predicting EUV dose are improved by using the first two-parameter linear regression model based on LUMO energy level and the first deprotonation efficiency pKa (DPS), the second two-parameter linear regression model based on LUMO energy level and the second deprotonation efficiency pKa (rearranged), and the second two-parameter linear regression model based on LUMO energy level and oxidation energy change ΔG. oxidation The third two-parameter linear regression model and the use of the LUMO energy level and total energy change ΔG total The fourth two-parameter linear regression model is better in all cases.

[0128] refer to Fig. 13B , which shows the change in total energy when using the LUMO level and the total energy change ΔG total The fourth two-parameter linear regression model is the correlation between the dose prediction value and the dose experimental value.

[0129] Fig.14is a flowchart illustrating a method of manufacturing a semiconductor device according to example embodiments.

[0130] refer to Fig.14 In the method of manufacturing a semiconductor device according to an example embodiment, a PAG used in an EUV lithography process is designed (operation S2100), and a semiconductor device is fabricated based on the result of the designed PAG (operation S2200). Fig.10 and Fig.11 The method of designing a PAG according to example embodiments is described to perform operation S2100 .

[0131] In operation S2200, a semiconductor device may be fabricated or manufactured by mask, wafer, testing, assembly, packaging, and / or the like. For example, a photoresist including the PAG designed in operation S2100 may be manufactured. For example, an EUV lithography process may be performed using a photoresist, a photomask, and the like, various other processes such as an etching process may be performed, and a desired pattern may be sequentially formed on a semiconductor substrate by these processes. Thereafter, a semiconductor device may be obtained in the form of a semiconductor chip by various additional processes.

[0132] Fig.15A and Fig. 15B are diagrams for describing a method of manufacturing a semiconductor device according to example embodiments.

[0133] refer to Fig.15A and Fig. 15B , conceptually illustrating an example of an EUV lithography process.

[0134] The organic CAR photoresist used in the positive tone development (PTD) method can be a composite including a polymer, a PAG, a photodegradable quencher (PDQ), etc. (or consisting of them). The portion of the polymer whose protective group has been removed by the acid generated by the PAG or the polymer can be easily dissolved in the solvent, resulting in a difference in solubility from the portion with the protective group remaining. In other words, in the exposed area, the PAG can generate an acid and the protective group of the polymer can be removed, resulting in a difference in solubility from the unexposed area, thereby forming a pattern.

[0135] When a dual-parameter linear regression model for EUV dose is obtained using the LUMO energy level and additional parameters according to example embodiments, the EUV dose can be efficiently predicted, and the prediction consistency can be improved or enhanced based on the dual-parameter linear regression model. In addition, new PAG molecules can be efficiently designed based on the method for predicting EUV dose according to example embodiments, and the new PAG molecules have excellent performance (e.g., high acid production rate) compared to existing PAG molecules and are used to form patterns in EUV lithography processes even at low EUV doses.

[0136] Although the example embodiments are described based on a two-parameter linear regression model, the example embodiments are not limited thereto and may be extended and applied by using an N-parameter linear regression model, where N is a positive integer greater than or equal to 3. For example, when N=3, the EUV dose may be predicted, and the PAG molecule may be designed based on the three-parameter linear regression model obtained by Equation 8.

[0137] [Formula 8]

[0138] Dose=α×LUMO+β×P1 after +γ×P2 after +δ

[0139] In Formula 8, “Dose” represents EUV dose, “LUMO” represents LUMO energy level, and P1 after Indicates the first additional parameter, P2 after represents the second additional parameter, and α, β, γ and δ represent coefficients, respectively. For example, α may correspond to the contribution of the LUMO energy level, β may correspond to the contribution of the first additional parameter, and γ may correspond to the contribution of the second additional parameter. For example, the first additional parameter and the second additional parameter may be the second deprotonation efficiency pKa (rearranged) and the oxidation energy change ΔG, respectively. oxidation , but example embodiments are not limited thereto.

[0140] In addition, although the example embodiments are described based on the method of predicting EUV dose and the method of designing PAG, the example embodiments are not limited thereto, and the example embodiments may be extended and applied to software development for predicting lithography properties and process characteristics such as dose, LER, LWR, CD variation, etc. based on various parameters. When designing a program for predicting lithography characteristics based on the physical properties of a material, it may be determined whether to use the example embodiments by checking the parameters used for dose prediction. For example, it may be determined whether to use the example embodiments by checking whether the mathematical formula according to the example embodiments is implemented in the program code.

[0141] As will be appreciated by those skilled in the art, the inventive concept may be embodied as a system, method, computer program product, and / or a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon. The computer-readable program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be any tangible medium capable of containing or storing a program for use by or in conjunction with an instruction execution system, device, or apparatus. For example, a computer-readable medium may be a non-transitory computer-readable medium.

[0142] Example embodiments may be applied to manufacture semiconductor devices and / or semiconductor integrated circuits. For example, example embodiments may be applied to systems such as personal computers (PCs), server computers, data centers, workstations, mobile phones, smart phones, tablet computers, laptop computers, personal digital assistants (PDAs), portable multimedia players (PMPs), digital cameras, portable game consoles, music players, video players, navigation devices, wearable devices, Internet of Things (IoT) devices, Internet of Everything (IoE) devices, e-book readers, virtual reality (VR) devices, augmented reality (AR) devices, robotic devices, drones, cars, etc.

[0143] One or more of the elements disclosed above may include or be implemented with processing circuits such as: hardware including logic circuits; hardware / software combinations, such as processors running software; or combinations thereof. For example, the processing circuits may more specifically include, but are not limited to, microprocessors, application processors (APs), central processing units (CPUs), arithmetic logic units (ALUs), digital signal processors (DSPs), microcomputers, field programmable gate arrays (FPGAs), systems on chips (SoCs), programmable logic units, microprocessors, application specific integrated circuits (ASICs), graphics processing units (GPUs), neural processing units (NPUs), and the like. The processor may be one processor that performs a defined function or multiple processors that perform a defined function together, such that the operation of each defined function may be divided among such processors.

[0144] The foregoing describes example embodiments and should not be construed as limiting thereof. Although some example embodiments have been described, those skilled in the art should readily appreciate that many modifications are possible in the example embodiments without materially departing from the novel teachings and advantages of the example embodiments. Therefore, all such modifications are intended to be included within the scope of the example embodiments defined in the claims. Therefore, it should be understood that the foregoing describes various example embodiments and should not be construed as limited to the specific example embodiments disclosed, and modifications to the disclosed example embodiments as well as other example embodiments are intended to be included within the scope of the appended claims.

Claims

1. A method for predicting extreme ultraviolet dose, the method being performed by at least one processor running a program code, the program code being stored in a non-transitory computer-readable medium, the method comprising: Analyze the entire photochemical reaction mechanism of photoacid generator molecules from photoacid generator cations to proton release under extreme ultraviolet exposure; obtaining the lowest unoccupied molecular orbital energy level by performing simulations for structural optimization of the photoacid generator cation; obtaining an additional parameter different from the lowest unoccupied molecular orbital energy level by performing simulations for structural optimization of at least one intermediate molecular structure formed by the entire photochemical reaction mechanism; and A two-parameter linear regression model for predicting the extreme ultraviolet dose is obtained based on the lowest unoccupied molecular orbital energy level and the additional parameters.

2. The method according to claim 1, wherein: In the entire photochemical reaction mechanism, The first molecular structure is formed by electron capture; forming a second molecular structure and a second-first molecular structure through internal excitation; A second-second molecular structure is formed by the second molecular structure or the second-first molecular structure; forming a third molecular structure from the first molecular structure; A fourth molecular structure is formed by the second molecular structure, the second-first molecular structure or the third molecular structure; as well as A fifth molecular structure is formed from the fourth molecular structure or the second-second molecular structure, and the fifth molecular structure corresponds to a molecular structure after the proton is released from the photoacid generator cation.

3. The method according to claim 2, wherein: The photoacid generator cation is a triphenylsulfonium cation.

4. The method according to claim 3, wherein: The first molecular structure corresponds to a structure in which the triphenylsulfonium cation is dissociated into a diphenyl sulfide molecule and a phenyl radical, The third molecular structure corresponds to a structure in which the phenyl radical decomposed from the first molecular structure attacks the ortho position of the phenyl ring in the diphenyl sulfide molecule, The fourth molecular structure corresponds to the oxidized structure of the third molecular structure, and The fifth molecular structure corresponds to a structure in which the triphenylsulfonium cation releases a proton and is rearranged.

5. The method according to claim 3, wherein: The second molecular structure corresponds to a structure in which the triphenylsulfonium cation is dissociated into a diphenyl sulfide radical cation with a phenyl radical, The second-first molecular structure corresponds to a structure in which the triphenylsulfonium cation is dissociated into a diphenyl sulfide molecule with a phenyl cation, and The second-second molecular structure corresponds to a structure in which a proton is released from the second molecular structure or the second-first molecular structure.

6. The method according to claim 2, wherein: The additional parameters include a total energy change from the third molecular structure to the fifth molecular structure.

7. The method according to claim 6, wherein: Obtaining the additional parameters includes: obtaining an oxidation energy variation by performing a simulation for structural optimization of the third molecular structure; obtaining a deprotonation energy variation amount by performing simulation for structural optimization of the fourth molecular structure; and A total energy change amount is obtained based on the oxidation energy change amount and the deprotonation energy change amount.

8. The method according to claim 2, wherein: The additional parameter includes an amount of change in oxidation energy from the third molecular structure to the fourth molecular structure.

9. The method according to claim 8, wherein: Obtaining the additional parameters includes: The oxidation energy variation amount is obtained by performing simulation for structural optimization of the third molecular structure.

10. The method according to claim 2, wherein: The additional parameter includes a first deprotonation efficiency from the second molecular structure to the second-second molecular structure.

11. The method according to claim 10, wherein: Obtaining the additional parameters includes: The first deprotonation efficiency is obtained by performing simulation for structural optimization of the second molecular structure.

12. The method according to claim 2, wherein: The additional parameter includes a second deprotonation efficiency from the fourth molecular structure to the fifth molecular structure.

13. The method according to claim 12, wherein: Obtaining the additional parameters includes: The second deprotonation efficiency is obtained by performing simulation for structural optimization of the fourth molecular structure.

14. The method according to claim 2, wherein: The photoacid generator cation has a structure in which at least a portion of each phenyl ring in the triphenylsulfonium cation is modified.

15. A method for designing a photoacid generator, the method being performed by running a program code by at least one processor, the program code being stored in a non-transitory computer-readable medium, the method comprising: using a first photoacid generator molecule to predict a first EUV dose in the EUV lithography process based on the EUV lithography process; using a second photoacid generator molecule to predict a second EUV dose in the EUV lithography process based on the EUV lithography process, the second photoacid generator molecule being different from the first photoacid generator molecule; as well as determining a suitability of the second photoacid generator molecule based on the first EUV dose and the second EUV dose, Wherein, predicting the first extreme ultraviolet dose includes: analyzing a first entire photochemical reaction mechanism of the first photoacid generator molecule from a first photoacid generator cation to release of a first proton under extreme ultraviolet exposure; obtaining a first lowest unoccupied molecular orbital energy level by performing simulations for structural optimization of the first photoacid generator cation; obtaining a first additional parameter different from the first lowest unoccupied molecular orbital energy level by performing simulation for structural optimization of at least one first intermediate molecular structure formed by the first overall photochemical reaction mechanism; and A first two-parameter linear regression model for predicting the first extreme ultraviolet dose is obtained based on the first lowest unoccupied molecular orbital energy level and the first additional parameter.

16. The method according to claim 15, wherein: Predicting the second extreme ultraviolet dose includes: analyzing a second entire photochemical reaction mechanism of the second photoacid generator molecule from a second photoacid generator cation to release of a second proton under extreme ultraviolet exposure; obtaining a second lowest unoccupied molecular orbital energy level by performing simulations for structural optimization of the second photoacid generator cation; obtaining a second additional parameter different from the second lowest unoccupied molecular orbital energy level by performing simulation for structural optimization of at least one second intermediate molecular structure formed by the second overall photochemical reaction mechanism; and A second two-parameter linear regression model for predicting the second extreme ultraviolet dose is obtained based on the second lowest unoccupied molecular orbital energy level and the second additional parameter.

17. The method of claim 15, wherein: The first photoacid generator molecule is an existing material that has been used in the EUV lithography process, and The second photoacid generator molecule is a target material redesigned for use in the extreme ultraviolet lithography process.

18. The method of claim 17, wherein: The first photoacid generator cation is a triphenylsulfonium cation, and The second photoacid generator cation has a structure in which at least a portion of each phenyl ring included in the triphenylsulfonium cation is modified.

19. The method according to claim 15, wherein: Determining the suitability of the second photoacid generator molecule includes: determining that the second photoacid generator molecule is suitable for use in the EUV lithography process if the second EUV dose is an improvement over the first EUV dose; or If the second EUV dose is not an improvement over the first EUV dose, then it is determined that the second photoacid generator molecule is not suitable for use in the EUV lithography process.

20. A method for manufacturing a semiconductor device, the method comprising: designing a photoacid generator for use in an extreme ultraviolet lithography process by executing a program code by at least one processor, the program code being stored in a non-transitory computer readable medium; as well as The semiconductor device is manufactured based on the result of designing the photoacid generator, Wherein, the photoacid generator is designed to include predicting a first EUV dose in the EUV lithography process when using a first photoacid generator molecule; predicting a second EUV dose in the EUV lithography process when a second photoacid generator molecule different from the first photoacid generator molecule is used; and determining a suitability of the second photoacid generator molecule based on the first EUV dose and the second EUV dose, Among them, the first extreme ultraviolet dose is predicted to include analyzing a first entire photochemical reaction mechanism of the first photoacid generator molecule from a first photoacid generator cation to release of a first proton under extreme ultraviolet exposure; obtaining a first lowest unoccupied molecular orbital energy level by performing simulations for structural optimization of the first photoacid generator cation; obtaining a first additional parameter different from the first lowest unoccupied molecular orbital energy level by performing simulation for structural optimization of at least one first intermediate molecular structure formed by the first overall photochemical reaction mechanism; and A first two-parameter linear regression model for predicting the first extreme ultraviolet dose is obtained based on the first lowest unoccupied molecular orbital energy level and the first additional parameter.

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